Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
summary: Electroencephalography (EEG) is a prominent non-invasive neuroimaging
technique providing insights into brain function. Unfortunately, EEG data
exhibit a high degree of noise and variability across subjects hampering
generalizable signal extraction. Therefore, a key aim in EEG analysis is to
extract the underlying neural activation (content) as well as to account for
the individual subject variability (style). We hypothesize that the ability to
convert EEG signals between tasks and subjects requires the extraction of
latent representations accounting for content and style. Inspired by recent
advancements in voice conversion technologies, we propose a novel contrastive
split-latent permutation autoencoder (CSLP-AE) framework that directly
optimizes for EEG conversion. Importantly, the latent representations are
guided using contrastive learning to promote the latent splits to explicitly
represent subject (style) and task (content). We contrast CSLP-AE to
conventional supervised, unsupervised (AE), and self-supervised (contrastive
learning) training and find that the proposed approach provides favorable
generalizable characterizations of subject and task. Importantly, the procedure
also enables zero-shot conversion between unseen subjects. While the present
work only considers conversion of EEG, the proposed CSLP-AE provides a general
framework for signal conversion and extraction of content (task activation) and
style (subject variability) components of general interest for the modeling and
analysis of biological signals.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
summary: Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique providing insights into brain function. Unfortunately, EEG data exhibit a high degree of noise and variability across subjects hampering generalizable signal extraction. Therefore, a key aim in EEG analysis is to extract the underlying neural activation (content) as well as to account for the individual subject variability (style). We hypothesize that the ability to convert EEG signals between tasks and subjects requires the extraction of latent representations accounting for content and style. Inspired by recent advancements in voice conversion technologies, we propose a novel contrastive split-latent permutation autoencoder (CSLP-AE) framework that directly optimizes for EEG conversion. Importantly, the latent representations are guided using contrastive learning to promote the latent splits to explicitly represent subject (style) and task (content). We contrast CSLP-AE to conventional supervised, unsupervised (AE), and self-supervised (contrastive learning) training and find that the proposed approach provides favorable generalizable characterizations of subject and task. Importantly, the procedure also enables zero-shot conversion between unseen subjects. While the present work only considers conversion of EEG, the proposed CSLP-AE provides a general framework for signal conversion and extraction of content (task activation) and style (subject variability) components of general interest for the modeling and analysis of biological signals.
id: http://arxiv.org/abs/2311.07788v1
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